smtprofiling

Imported from FStarLang/proof-copilot (plugins/proof-copilot/skills/smtprofiling/SKILL.md). Install upstream with npx skills add FStarLang/proof-copilot --skill smtprofiling. Copyright stays with the author.

Invocation

This skill is used when:

• Verifying F* (.fst) or interface (.fsti) files
• Debugging verification failures and proof performance
• Especially when proofs require high rlimits or fail unpredictably
• A proof requires rlimit > 20 (auto-invoke this skill rather than escalating)
• A proof that previously passed starts failing (regression)
• Build time exceeds expectations for a module

Core Operations

Collect an .smt2 file for problematic proof

Wrap the part of the program to diagnose proof failures with

#push-options "--log_queries --z3refresh --query_stats --split_queries always"
let definition_to_be_debugged ...
#pop-options

Run F* on the file (with appropriate include paths)

fstar.exe Module.fst

The log messages will show the name of the .smt2 file logged for each proof obligation.

Using --z3refresh together with --log_queries produces a self-contained .smt2 file per query (each query starts with a fresh Z3 process). This is critical for isolating expensive sub-goals: each .smt2 file can be profiled independently.

Using --split_queries always decomposes compound VCs into individual sub-goals. This lets you identify exactly which conjunct of a proof obligation is expensive. Cross-reference the query number in the Query-stats output with the .smt2 file number to map each timing to its isolated file.

Verify .smt2 file independently of F*

Find z3 in the path. It might be named z3-4.13.3, z3-4.15.1 etc., with a version number suffix

z3 queries-myquery.smt2

You can add z3 options like smt.qi.profile=true to see which quantifiers were firing too much and what to do about it

Interpreting a quantifier profile

The Z3 quantifier profile is printed to stderr. Capture it with:

z3 smt.qi.profile=true queries-myquery.smt2 2> qi_profile.txt

Parse the output to get per-quantifier totals:

awk '/\[quantifier_instances\]/ {
  name = $2; count = $4; total[name] += count;
} END {
  for (n in total) printf "%8d %s\n", total[n], n;
}' qi_profile.txt | sort -rn | head -20

Some quantifiers in F*'s SMT encoding always fire a lot---this is by design. Typically, quantifiers with unqualified names (Box_bool_proj_0) or those with names in Prims, or FStar. Pervasives will fire a lot.

Look for quantifiers in modules that are in files that you authored that are firing a lot---these might signify that you should write a pattern for those quantifiers, or control their instantiation explicitly.

Common quantifier cascade patterns

A quantifier cascade is when one quantifier instantiation produces terms that trigger another quantifier, which produces more terms, creating a positive feedback loop. This is the most common cause of queries taking minutes instead of seconds.

Signs of a cascade:

• One or two quantifiers have 10,000+ instantiations while everything else has < 100
• Query takes very long wall-clock time but uses modest rlimit
• The dominant quantifier has a single-term pattern like [SMTPat (f x y)] and its conclusion introduces new terms that match the pattern of another quantifier

Example cascade found in EverParse:

1. cbor_map_equal unfolds to forall k. cbor_map_get m1 k == cbor_map_get m2 k
2. Each cbor_map_get produces cbor_map_defined terms
3. cbor_map_defined_alt has [SMTPat (cbor_map_defined k f)] and introduces exists v. cbor_map_mem (k,v) f
4. Existential skolemization creates new ground terms
5. New terms trigger more pattern matches → 64,000+ instantiations

Fixing quantifier cascades

Option 1: Remove the SMTPat and add a bring_ helper

Remove [SMTPat ...] from the offending lemma. Add a wrapper that re-introduces the quantifier with its pattern in a controlled scope:

// Original: always active, causes cascades
let problematic_lemma (x: t) (y: t)
: Lemma (some_prop x y)
  [SMTPat (f x y)]    // REMOVE THIS
= ...

// New: call bring_ only where needed
let bring_problematic_lemma ()
: Lemma (forall (x: t) (y: t) . {:pattern (f x y)} some_prop x y)
= Classical.forall_intro_2 problematic_lemma

Then call bring_problematic_lemma () only in the specific proofs that need it, rather than polluting all proofs globally.

Option 2: Use --using_facts_from to prune per-function

Surgically remove a lemma from Z3's context for a specific function without modifying the lemma itself:

#push-options "--using_facts_from '* -Module.Name.problematic_lemma'"
let my_expensive_function ...
#pop-options

This is useful for quick experimentation before committing to Option 1. Verify the quantifier is actually removed by checking the .smt2 file.

Option 3: Use multi-pattern triggers

Change a single-term pattern to a conjunctive multi-pattern so the lemma only fires when multiple terms are present:

// Before: fires whenever (f x y) appears
[SMTPat (f x y)]

// After: fires only when both (f x y) AND (g x) are present
[SMTPat (f x y); SMTPat (g x)]

Systematic profiling workflow

For large projects, profile the entire build:

# 1. Full build with --query_stats
make -j$(nproc) OTHERFLAGS="--query_stats" 2>&1 | tee build_profile.log

# 2. Find top files by SMT time
grep -a "Query-stats" build_profile.log | grep "succeeded" \
  | sed -n 's/(\([^(]*\)\.\(fst\|fsti\)([^)]*)).*succeeded in \([0-9]*\) milliseconds.*/\1.\2 \3/p' \
  | awk '{f[$1]+=$2} END {for(k in f) print f[k], k}' | sort -rn | head -20

# 3. Find top individual queries
grep -a "Query-stats" build_profile.log | grep "succeeded" \
  | sed -n 's/.*Query-stats (\([^)]*\)).*succeeded in \([0-9]*\) milliseconds.*rlimit \([0-9]*\) (used rlimit \([0-9.]*\)).*/\2\t\4\t\3\t\1/p' \
  | sort -rn | head -20

# 4. For the most expensive query, isolate it:
#push-options "--log_queries --z3refresh --query_stats --split_queries always"

# 5. Profile the isolated .smt2 file:
z3 smt.qi.profile=true queries-Module-NNN.smt2 2> qi_profile.txt

# 6. Parse the profile to find the dominant quantifier(s)

F* options for controlling SMT performance

Tightening fuel, ifuel, and rlimit

After fixing expensive queries, tighten the settings:

• Reduce fuel and ifuel to the minimum that works (prefer 0-2)
• Reduce rlimit to 2-4x the used rlimit reported by --query_stats
• High fuel (>2) causes exponential unfolding; replace with explicit lemma calls
• High ifuel (>2) causes excessive inversion; add explicit match/inversion steps

Reusing Proof Strategies for Stabilizing Proofs

When stuck on a proof performance problem, search for commits in the same project that successfully stabilized similar proofs. github.com/FStarLang/AlgoStar is a good example of a project with many proof performance commits to mine for techniques.:

Study what techniques were used — factoring lemmas, adding patterns, using calc proofs, adding opaque_to_smt, using --using_facts_from — and apply the same techniques to the current problem.

Proof Stabilization Catalog

The following catalog is mined from FStarLang/AlgoStar (897 commits of verified CLRS algorithm implementations in F*/Pulse). These are proven techniques organized by type of problem.

Technique 1: Factor Large Modules Into Smaller Ones

Problem: Large .fst files make Z3 slow because every function's VC carries the context of all preceding definitions.

Solution: Extract groups of related lemmas into separate modules.

Case study (Prim MST):

• Prim.Impl.fst was 2267 lines with 76 inline math lemmas — slow and fragile
• Factored into: Prim.Greedy.fst (1010 lines, 41s) for all greedy safety lemmas; Prim.KeyInv.fst (496 lines, 4s) for the key invariant module; Prim.Defs.fst (68 lines, 1.5s) for basic definitions
• Prim.Impl.fst shrank to 938 lines (59% reduction)
• See commits d1665a6, aada386 in FStarLang/AlgoStar

Case study (Heapsort):

• Deduplicated _upto lemma proofs by having non-_upto versions delegate to the general versions — eliminated one copy of an expensive perm_prefix_bounded_aux proof
• Lemmas.fst: 238s → 63s (74% reduction)
• See commit 29242fb in FStarLang/AlgoStar

Technique 2: Use #restart-solver Between Key Functions

Problem: Z3's accumulated state from prior functions pollutes the context for subsequent proofs, causing slowness or spurious failures.

Solution: Place #restart-solver between large functions to reset Z3.

Case study (Heapsort):

• Added #restart-solver before build_max_heap
• Impl.fst: 445s → 241s (46% reduction)
• See commit 29242fb in FStarLang/AlgoStar

Case study (Kruskal NL arithmetic):

• NL arithmetic (u * n + v < n * n) worked in isolation but failed in-context due to accumulated Z3 state
• #restart-solver between key functions fixed it
• See commit 01e2e8d in FStarLang/AlgoStar

Technique 3: Replace --z3refresh with --split_queries always

Problem: --z3refresh starts a new Z3 process per query (expensive); proofs need high rlimit.

Solution: --split_queries always decomposes VCs into smaller queries without the overhead of process restarts, often allowing lower rlimits.

Case study (CountingSort):

• Switched from --z3rlimit 800 --z3refresh to --z3rlimit 400 --split_queries always
• 50% rlimit reduction with same reliability
• See commit 5e35796 in FStarLang/AlgoStar

Technique 4: Opaque Bundles with Explicit Instantiation Lemmas

Problem: Complex predicates cause Z3 to spend time unfolding definitions it doesn't need.

Solution: Make predicates [@@"opaque_to_smt"] and provide explicit _at instantiation lemmas (for init, after_update, etc.).

Case study (Prim KeyInv):

• key_inv, ims_finite_key, parent_in_mst all made opaque
• Explicit key_inv_init, key_inv_after_update, key_inv_at lemmas for controlled instantiation
• parent_in_mst_after_update uses introduce forall + 3-way case split with explicit lemma calls — no Z3 quantifier matching needed
• Entire module verifies in 2.6s
• See commit aada386 in FStarLang/AlgoStar

Technique 5: Wrap NL Arithmetic in Helper Functions

Problem: Non-linear arithmetic expressions like u * n + v cause Z3 to enter expensive NL reasoning.

Solution: Bundle NL index computation in a function with refinement types, then use FStar.Math.Lemmas for explicit NL bounds.

Case study (Kruskal adj_weight):

• adj_weight bundles u * n + v index in a refined function type
• FStar.Math.Lemmas.lemma_mult_lt_right for explicit NL bounds
• Unblocked a proof that was stuck for days on NL arithmetic
• See commits bf00c94 → 01e2e8d in FStarLang/AlgoStar

Technique 6: Aggressive rlimit Reduction After Proof Works

Problem: Proofs initially written with high rlimits are flaky — they may break with unrelated changes to the Z3 context.

Solution: Once a proof works, systematically reduce rlimit to the minimum needed (target ≤ 10, tolerate up to 200 for complex Pulse VCs).

Case study (ch22 BFS/DFS):

• queue_bfs: 2400 → 200 (92% reduction)
• maybe_discover: 600 → 200 (67% reduction)
• dfs_visit: 800 → 200 (75% reduction)
• See commit 896a145 in FStarLang/AlgoStar

Case study (ch26 MaxFlow):

• max_flow: 600 → 50 (92% reduction!)
• find_bottleneck_imp, augment_imp: 200 → 80 (60% reduction)
• See commit eeb0fdb in FStarLang/AlgoStar

Technique 7: Hoist Heavy Sub-computations Out of Loop Bodies

Problem: Large Pulse functions take long to verify, with every proof obligation going issuing its own Z3 query, and with the assumptions in the context accumulating through the context of the function.

Solution: Extract helpers: factor fn for sub-tasks, call them from the enclosing function, allowing the proof to proceed more modularly.

Case study (Prim MST):

• find_min_vertex: hoisted to separate fn → 3.5min build (was 4.5min)
• prim_step: hoisted to separate fn → cleaner invariant structure
• update_keys: hoisted to separate fn → focused VC
• See commits 91e8b43, fdbf5bc in FStarLang/AlgoStar

Technique 8: Set Fuel Precisely per Lemma

Problem: Fuel 2 causes Z3 to try unfolding recursion two levels deep, which can be exponentially expensive with multiple recursive definitions.

Solution: Set --fuel 0 --ifuel 0 as the module default, then use #push-options "--fuel N" only on lemmas that need it.

Case study (ch09 PartialSelectionSort):

• remove_element_count_le/lt needed --fuel 4 explicitly
• With default fuel 2, Z3 spent 200s on fuel-retry; with explicit fuel 4, 29s
• Full build: 7min → 1min (7× speedup)
• See commit 4c158fa in FStarLang/AlgoStar

Technique 9: Use introduce forall Instead of SMT Quantifier Matching

Problem: Complex universal quantifiers with tricky patterns — Z3 fails to find the right instantiation.

Solution: Use F*'s introduce forall tactical sugar with explicit case splits and lemma calls, bypassing Z3's pattern matching entirely.

Case study (Prim parent_in_mst_after_update):

• Uses introduce forall + 3-way case split (v=u, v in MST, v not in MST)
• Each case handled with explicit lemma call
• No SMT pattern needed — proof is fully explicit
• See commit aada386 in FStarLang/AlgoStar

Technique 10: Avoid Wrapper Types in Quantifier Bodies

Problem: Using helper functions inside quantifier bodies prevents Z3 from matching patterns.

Solution: Use raw expressions (e.g., Seq.index) in opaque predicate bodies rather than convenience wrappers.

Case study (Prim KeyInv):

• key_inv initially used swt (safe weight accessor) in its quantifier
• Changed to raw w*n+v / Seq.index — matches what prim_safe_add_vertex needs
• Entire module verifies in 3s
• See commit c992174 in FStarLang/AlgoStar

Quantifier Instantiation Threshold

If Z3 fails to instantiate long quantifier chains (proof fails even though the required facts are in scope):

• The smt.qi.eager_threshold controls how aggressively Z3 instantiates quantifiers. Default is 10.
• If confident there are no instantiation loops, try a higher value: --z3smtopt '(set-option :smt.qi.eager_threshold 100)'
• Symptoms: proof works at high rlimit but fails at low rlimit because Z3 gives up instantiating before finding the proof
• Diagnosis: check the quantifier profile for quantifiers with exactly eager_threshold instantiations — these were cut off

More information on SMT profiling

Find PoP-in-FStar at https://github.com/FStarLang/PoP-in-FStar

See https://fstar-lang.org/tutorial/book/under_the_hood/uth_smt.html#profiling-z3-and-solving-proof-performance-issues

The section under_the_hood/uth_smt.rst contains information about F*'s SMT encoding and how to profile.